Video Forgery Detection with Optical Flow Residuals and Spatial-Temporal Consistency
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909716038287360 |
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| author | Xue, Xi Suzuki, Kunio Goswami, Nabarun Shintate, Takuya |
| author_facet | Xue, Xi Suzuki, Kunio Goswami, Nabarun Shintate, Takuya |
| contents | The rapid advancement of diffusion-based video generation models has led to increasingly realistic synthetic content, presenting new challenges for video forgery detection. Existing methods often struggle to capture fine-grained temporal inconsistencies, particularly in AI-generated videos with high visual fidelity and coherent motion. In this work, we propose a detection framework that leverages spatial-temporal consistency by combining RGB appearance features with optical flow residuals. The model adopts a dual-branch architecture, where one branch analyzes RGB frames to detect appearance-level artifacts, while the other processes flow residuals to reveal subtle motion anomalies caused by imperfect temporal synthesis. By integrating these complementary features, the proposed method effectively detects a wide range of forged videos. Extensive experiments on text-to-video and image-to-video tasks across ten diverse generative models demonstrate the robustness and strong generalization ability of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00397 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Video Forgery Detection with Optical Flow Residuals and Spatial-Temporal Consistency Xue, Xi Suzuki, Kunio Goswami, Nabarun Shintate, Takuya Computer Vision and Pattern Recognition The rapid advancement of diffusion-based video generation models has led to increasingly realistic synthetic content, presenting new challenges for video forgery detection. Existing methods often struggle to capture fine-grained temporal inconsistencies, particularly in AI-generated videos with high visual fidelity and coherent motion. In this work, we propose a detection framework that leverages spatial-temporal consistency by combining RGB appearance features with optical flow residuals. The model adopts a dual-branch architecture, where one branch analyzes RGB frames to detect appearance-level artifacts, while the other processes flow residuals to reveal subtle motion anomalies caused by imperfect temporal synthesis. By integrating these complementary features, the proposed method effectively detects a wide range of forged videos. Extensive experiments on text-to-video and image-to-video tasks across ten diverse generative models demonstrate the robustness and strong generalization ability of the proposed approach. |
| title | Video Forgery Detection with Optical Flow Residuals and Spatial-Temporal Consistency |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.00397 |